With the strong computation capability, NUMA-based multi-GPU system is a promising candidate to provide sustainable and scalable performance for Virtual Reality. However, the entire multi-GPU system is viewed as a single GPU which ignores the data locality in VR rendering during the workload distribution, leading to tremendous remote memory accesses among GPU models. By conducting comprehensive characterizations on different kinds of parallel rendering frameworks, we observe that distributing the rendering object along with its required data per GPM can reduce the inter-GPM memory accesses. However, this object-level rendering still faces two major challenges in NUMA-based multi-GPU system: (1) the large data locality between the left and right views of the same object and the data sharing among different objects and (2) the unbalanced workloads induced by the software-level distribution and composition mechanisms. To tackle these challenges, we propose object-oriented VR rendering framework (OO-VR) that conducts the software and hardware co-optimization to provide a NUMA friendly solution for VR multi-view rendering in NUMA-based multi-GPU systems. We first propose an object-oriented VR programming model to exploit the data sharing between two views of the same object and group objects into batches based on their texture sharing levels. Then, we design an object aware runtime batch distribution engine and distributed hardware composition unit to achieve the balanced workloads among GPMs. Finally, evaluations on our VR featured simulator show that OO-VR provides 1.58x overall performance improvement and 76% inter-GPM memory traffic reduction over the state-of-the-art multi-GPU systems. In addition, OO-VR provides NUMA friendly performance scalability for the future larger multi-GPU scenarios with ever increasing asymmetric bandwidth between local and remote memory.

中文翻译：

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OO-VR：面向未来基于 NUMA 的多 GPU 系统的 NUMA 友好的面向对象 VR 渲染框架

凭借强大的计算能力，基于 NUMA 的多 GPU 系统是为虚拟现实提供可持续和可扩展性能的有希望的候选者。然而，整个多GPU系统被视为单个GPU，在工作负载分配过程中忽略了VR渲染中的数据局部性，导致GPU模型之间大量的远程内存访问。通过对不同类型的并行渲染框架进行全面的表征，我们观察到，将渲染对象及其所需的数据分配给每个 GPM 可以减少 GPM 间的内存访问。但是，这种对象级渲染在基于 NUMA 的多 GPU 系统中仍然面临两大挑战：(1) 同一对象左右视图之间的大数据局部性以及不同对象之间的数据共享； (2) 软件级分布和组合机制导致的工作负载不平衡。为了应对这些挑战，我们提出了面向对象的 VR 渲染框架 (OO-VR)，该框架进行软件和硬件协同优化，为基于 NUMA 的多 GPU 系统中的 VR 多视图渲染提供 NUMA 友好的解决方案。我们首先提出了一种面向对象的 VR 编程模型，以利用同一对象的两个视图之间的数据共享，并根据对象的纹理共享级别将对象分组。然后，我们设计了一个对象感知运行时批处理分发引擎和分布式硬件组合单元，以实现 GPM 之间的负载均衡。最后，对我们的 VR 特色模拟器的评估表明，与最先进的多 GPU 系统相比，OO-VR 提供 1.58 倍的整体性能提升和 76% 的 GPM 间内存流量减少。此外，OO-VR 为未来更大的多 GPU 场景提供了 NUMA 友好的性能可扩展性，本地和远程内存之间的非对称带宽不断增加。